High intensity focused ultrasound transducer optimization

ABSTRACT

When planning magnetic resonance (MR) guided high intensity focused ultrasonic (HIFU) therapy, HIFU transducer element parameters are optimized as a function of 3D MR data describing a size, shape, and position of a region of interest (ROI) ( 146 ) and any obstructions ( 144 ) between the HIFU transducer elements and the ROI ( 146 ). Transducer element phases and amplitudes are adjusted to maximize HIFU radiation delivery to the ROI ( 146 ) while minimizing delivery to the obstruction ( 144 ). Additionally or alternatively, transducer elements are selectively deactivated if the obstruction ( 144 ) is positioned between the ROI ( 146 ) and a given transducer element.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.16/102,970 filed Aug. 14, 2018, entitled “High Intensity FocusedUltrasound Transducer Optimization”, which is a continuation of U.S.patent application Ser. No. 13/515,519 filed Jun. 13, 2012 (now U.S.Pat. No. 10,058,717), entitled “High Intensity Focused UltrasoundTransducer Optimization,” which is the U.S. National Stage ofInternational Application No. PCT/IB2010/055640 filed Dec. 7, 2010,entitled “High Intensity Focused Ultrasound Transducer Optimization,”which claims the benefit of U.S. Provisional Application Ser. No.61/290,268 filed Dec. 28, 2009, each of which are hereby incorporatedherein by reference in their entirety.

DESCRIPTION

The present application finds particular utility in magnetic resonance(MR)-guided high intensity focused (HIFU) ablation procedures andsystems. However, it will be appreciated that the described technique(s)may also find application in other types of therapy planning systems,other image guided therapy systems, and/or other medical applications.

Liver cancer is one of the most usual tumor types, and is especiallydifficult to operate because it tends to bleed strongly. Ablativeprocedures have therefore become common for its treatment, includingthermal ablation with radio frequency (RF) or laser probes, cryoblation,etc.

The use of High Intensity Focused Ultrasound (HIFU) has gradually becomemore popular. One reason is that MR imaging can be used for monitoringtissue temperature during the procedure, making it less risky. For liverablation there are two major problems: The liver has a lot of bloodflow, and it moves around with the patient respiration. Another problemis that the liver is situated behind the ribs, which obstruct HIFUtransmission.

Generally speaking, one problem associated with MR-guided HIFUprocedures is that it is difficult to distribute the applied heat in theright places: On one hand the ROI, e.g. a tumor, needs to be ablated. Onthe other hand, the regions in front of it (near field), or behind it(far field), as seen from transducer, need to be preserved. In the caseof the liver this problem is aggravated by the fact that the liver isefficiently cooled by the blood flow, increasing the demand onultrasonic power, and at the same time the ribs limit accessibility tothe ROI. The rib problem is twofold: First, the part of the beam hittingthe ribs cannot reach the ROI, and second, the ribs are sensitive toultrasonic radiation because they absorb it effectively and thereforeeasily overheat.

Classical planning procedures are mainly designed for sonication oforgans that can be reached directly without beaming between ribs, e.g.the uterus. The focal spot of the transducer is steered to the ROI. Inthe most sophisticated systems both mechanical, using motors, andelectronic steering, using phased transducer element arrays, are used.

Presently, the state of the art in treatment planning is that a planningsoftware tool is used to describe the desired transducer position andtreatment cells using MRI images from the patient. The softwarevisualizes the acoustic path on top of the MRI images in order to revealwhether any critical organs are located in the beam path or within thesafety margins. The acoustic beam is modeled simply as a geometric conefrom the transducer to the focal point and into the far field. Duringtreatment, low-energy sonications are used to detect and manuallycorrect spatial deviation from the targeted focus, and thermalmonitoring is used as a feedback to control the thermal heating.

General numerical methods for solving wave propagation are known in theliterature. In the field of medical ultrasound, the most prominentapproaches are time-domain based methods, the finite-element method, andthe various derivates of the Rayleigh integral.

In conventional treatment and treatment planning techniques, theacoustic path is assumed to be through water (e.g., a water phantom).However, there are various materials and tissues with different acousticproperties along the acoustic path through a human subject or patient.In particular, the role of the subcutaneous fat as a source of acousticdistortion is well known. Refraction at interfaces causes shift of theacoustic focus from the targeted position, and phase deviation due todifferent acoustic path lengths degrades the sharpness of the spot.These problems are conventionally dealt with using trial-and-errormanual corrections and thermal feedback. These tasks can be alleviatedby properly modeling the acoustic path.

A conventional treatment plan is generated using geometrical cones, sothat the whole relevant volume is covered by the ultrasonic waves.However, often significantly-distorting objects (i.e. obstructions) arelocated along the acoustic path, such as bones. Bones give a rise toreflections and wave diffraction, distorting the shape of the focus, andcreating potential detriment to both the patient and the ultrasonictransducer.

For simulation techniques with accurate numerical methods, such as afinite element method or a Rayleigh integral, the geometry of theproblem is typically described as a discrete mesh, consisting of finitegeometric primitives such as triangles. The dimensions of the primitivesare a fraction of the wavelength. A common characteristic of theacoustic modeling problem is that the considered structures arerelatively large in terms of acoustic wavelengths, resulting incorrespondingly large meshes. Details vary according to the specificnumerical method employed, but common to all these methods areinteractions of the small primitives with each other. For large meshes,this results in such long simulation times that the technique may not becost-effective in an interactive fashion. For instance, one popular wayto use such techniques is to use a working day to plan and prepare thesimulation task, and to perform the actual computation overnight orduring a weekend.

There is an unmet need in the art for systems and methods thatfacilitate automated optimization of HIFU transducer element propertiesto exploit intercostal space in the patient as an ablation path, and thelike, thereby overcoming the deficiencies noted above.

In accordance with one aspect, a therapy planning tool that facilitatesmagnetic resonance (MR) guided high intensity focused ultrasonic (HIFU)ablation planning includes a processor that executes computer-executableinstructions for optimizing HIFU transducer element transmission, theinstructions comprising evaluating transducer data including transducerelement position, geometry, and acoustic parameter information. Theinstructions further include evaluating 3D MR data including ROI datadescribing a size, shape, and position of a region of interest (ROI) tobe ablated, and obstruction data describing a size, shape, and positionof an obstruction between one or more HIFU transducer elements and theROI. Additionally, the instructions include executing an optimizer thatmaximizes HIFU waveform delivery the ROI while minimizing HIFU waveformdelivery to the obstruction and surrounding tissue. The planning toolfurther includes a memory that stores the computer-executableinstructions, the transducer data, MR data, and a plurality of optimizedHIFU parameters.

According to another aspect, a method of magnetic resonance (MR) guidedhigh intensity focused ultrasonic (HIFU) ablation planning includesevaluating transducer data including transducer element position,geometry, and acoustic parameter information, and evaluating 3D MR dataincluding ROI data describing a size, shape, and position of a region ofinterest (ROI) to be ablated, and obstruction data describing a size,shape, and position of an obstruction between one or more HIFUtransducer elements and the ROI. The method further includes executingan optimizer that maximizes HIFU waveform delivery the ROI whileminimizing HIFU waveform delivery to the obstruction and surroundingtissue.

According to another aspect, a method of performing an in-situsonication simulation for an MR-guided high intensity focused ultrasound(HIFU) ablation procedure includes generating a patient-specificacoustic path model, presenting the acoustic path model to a user via auser interface, and receiving user input regarding adjustments to atleast one of a position of one or more HIFU transducer elements and atransmission phase and amplitude of the one or more HIFU transducerelements. The method further includes simulating a HIFU sonication of aregion of interest (ROI) using the acoustic path model and the userinput.

One advantage is reduction of HIFU exposure in healthy tissue.

Another advantage resides in maximizing HIFU exposure in the ROI.

Another advantage resides in using acoustic simulations to model focalshape and monitor stray fields.

Another advantage resides in the ability to avoid excessive heating ofsensitive tissues.

Still further advantages of the subject innovation will be appreciatedby those of ordinary skill in the art upon reading and understanding thefollowing detailed description.

The drawings are only for purposes of illustrating various aspects andare not to be construed as limiting.

FIG. 1 illustrates a planning tool that facilitates optimizingtransducer element phase, amplitude, position, etc., and performing fastacoustic simulations in situ during ultrasonic treatment planning.

FIG. 2 illustrates a process flow for optimizing transmission parameters(e.g., amplitudes and phases) corresponding to a given HIFU transducerposition, as well as geometry and acoustic parameters while accountingfor the ablation ROI size and position and the ribs' locations.

FIG. 3 illustrates a process flow for the optimization procedure basedon a spatial impulse response technique.

FIG. 4 illustrates an example of a HIFU transducer array positionedadjacent a patient's skin, with a plurality of ribs impeding thetransmission of ultrasonic waves to an ROI to be ablated.

FIG. 5 illustrates an example of superposition amplitude and phaseresults for ultrasonic waveforms or rays so that multiple zones of theROI can be ablated concurrently as the HIFU array transmits ultrasonicwaves through the patient's skin and past the ribs, minimizing theoverall therapy time.

FIG. 6 illustrates a method for optimizing HIFU transducer positioningduring MR-guided tissue ablation procedures that utilize intercostalspaces, such as liver ablation.

FIG. 7 illustrates a method for optimizing HIFU transducer positioning.

FIG. 8 illustrates a conceptual arrangement with the HIFU array, theribs, and the ROI for a given position of the array.

FIG. 9 illustrates a method of performing stochastic acousticsimulation, in accordance with one or more aspects described herein.

FIG. 10 illustrates a method of performing acoustic simulation withplanar approximation, in accordance with one or more aspects describedherein.

FIG. 11 illustrates a method for estimating the contributions of asubset of all transducer elements, in accordance with one or moreaspects described herein.

Systems and methods are disclosed herein for optimizing selection ofHIFU transducer transmitting parameters and position during MR-guidedtissue ablation procedures that utilize intercostal spaces, such asliver ablation. The optimization procedure accounts for the HIFUtransducer position, geometry and acoustic parameters. It also accountsfor the ablation region of interest (ROI) size and position as well asribs' locations in 3D. The ribs' locations are determined throughsegmentation of high resolution MR data. The optimization procedureyields the amplitude and phase for each element of the transducer, aswell as a series of five degrees of freedom transducer locations (threedimensions plus pitch and yaw) together with a corresponding list ofdeactivated elements per position. The amplitudes and phases assuremaximum heat deposition in the ROI and minimum on the ribs. It should benoted that there is no direct element shutoff but rather optimalamplitudes and phases applied to all transducer elements. In addition,the optimization procedure yields superposition amplitude and phaseresults that ablate multiple zones of the ROI concurrently, minimizingthe overall therapy time.

In another embodiment, each transducer position is associated with agiven HIFU exposure time and energy such that after sonication from allpositions the entire ROI is ablated. The transducer positions can besorted in the descending order of their ROI coverage so that thetreatment starts from the position with best coverage. In anotherembodiment, heating in superficial skin layers can be minimized bysorting the transducer positions such that there is minimal overlapbetween consecutive active aperture footprints.

With reference to FIG. 1 , a planning tool 10 is illustrated thatfacilitates optimizing transducer element phase, amplitude, position,etc., and performing fast acoustic simulations in situ during ultrasonictreatment planning. The tool 10 includes a processor 12 that executes,and a memory or computer-readable medium 14 that stores,computer-executable instructions for performing the various functions,methods and/or algorithms described herein. In one embodiment, theprocessor 12 includes a parallel processing architecture. The toolfurther includes a user interface (UI) 16 (e.g., a monitor, computerterminal, workstation, etc.) via which information is presented to andreceived from a user or operator. The tool is coupled to each of ahigh-intensity focused ultrasound (HIFU) device 18 and a magneticresonance (MR) scanner 20.

The memory includes HIFU transducer data 22 or information, includingtransducer element position, geometry, and acoustic parameterinformation. ROI data 24 and obstruction data 26 are provided by the MRscanner, and stored in the memory as well. The ROI data describes a 3Dsize and shape of a region of interest (e.g., an ablation target), whilethe obstruction data describes a position or location and shape of anobstruction (e.g., a rib or other bone) between the transducer elementsand the ROI. The HIFU device also provides transducer element phase andamplitude information 28, which is stored in the memory. The processoranalyzes spatial impulse response information (e.g., from probes coupledto the ROI and obstruction), and executes a Fourier transform algorithm30 for a given continuous wave frequency, which is input into a phaseand amplitude optimizer 32 (e.g., computer-executable instructions thatare executed by the processor 12). The processor also generates aninitial solution for an amplitude and phase at which each transducerelement is to transmit, and an objective function that is input to thephase an amplitude optimizer, which then generates an optimal solutionfor the phase and amplitude of each transducer to ablate the ROI whileminimizing heat transfer to surrounding tissue and/or theobstruction(s). Additionally, the memory stores superposition results34, which are generated by the processor 12 to superposition theamplitude and phase of multiple elements in order to facilitateconcurrent ablation of multiple zones of the ROI. These aspects aredescribed in greater detail with regard to FIGS. 2-5 , below.

The memory also includes a position optimizer 36 (e.g., a set ofcomputer-executable instructions or the like) that receives transducerdata 22 related to a position of each transducer element along a surfaceof a patient, and relative to the ROI and any obstruction. The positionoptimizer, when executed by the processor 12, deactivates transducerelements that have a line of sight that passes through an obstruction onthe way to the ROI. In this manner, only elements with an unimpeded lineof sight to the ROI remain active and transmit during ablation. Theseaspects are described further with regard to FIGS. 6-8 , below.

According to another embodiment, the memory includes an in situ ablationsimulator 38 (e.g., computer-executable instructions that are executedby the processor 12) that performs fast acoustic simulations during insitu treatment planning, one which employs an acoustic path model 40 ofthe patient anatomy during an ablation simulation. Three suitablealgorithms are described herein for carrying out the in situ simulation,including a stochastic simulation algorithm 42, an approximationalgorithm 44, and an estimation algorithm 46. The stochastic simulationalgorithm employs a phonon buffer 48 that stores phonon informationduring the simulation procedure. These features are described in greaterdetail with regard to FIGS. 9-11 .

In one embodiment, the acoustic path model includes a water tank, Mylarfoil, gel pad, and patient anatomy. The acoustic parameters, typicallywave velocity, wave attenuation, and tissue density, are specified foreach point of the simulation volume. This is done by segmenting thevolume into homogeneous subvolumes, and specifying a set of acousticparameters for each subvolume. In one embodiment, a user provides thesegmentation information manually based on the planning images (e.g.,generated using the MR scanner 20). For example, for fibroid treatmentthe location and approximate thickness of the subcutaneous fat layer maybe useful. In another embodiment, automatic segmentation algorithms (notshown) are used to do the segmentation from the planning images taken insitu.

In other embodiments, such as in applications where either the qualityof the MRI images or the computational requirements make in situautomatic segmentation unfeasible, segmentation can be done offlinebased on previously generated images. Additionally, imaging modalitiesother than MRI can be used as a basis for segmentation.

To complete the segmentation, each subvolume is provided specificacoustic parameters. In one embodiment, the user enters the parametervalues using the UI 16. The values can be determined based on the tissuefrom a table of typical values.

FIGS. 2-5 and the related description relate to optimization oftransducer transmission parameters to exploit intercostal spacing duringMR-guided tissue ablation.

FIG. 2 illustrates a process flow for optimizing transmission parameters(e.g., amplitudes and phases) corresponding to a given HIFU transducerposition, as well as geometry and acoustic parameters while accountingfor the ablation ROI size and position and the ribs' locations. Inaddition, the optimization process of FIG. 2 optimizes the overalltherapy time by providing amplitudes and phases that facilitateconcurrent ablation of multiple zones of the ROI. At 100, several typesof input information are received (e.g., by a processor performing theoptimization). The input information includes HIFU transducer position,geometry, and acoustic parameters, as well as ablation ROI size andposition. The input information also includes 3D rib positioninformation.

At 102, optimization is performed, and includes optimizing an amplitudeand phase for each of a plurality of transducer elements. Theoptimization further includes generating and/or optimizing superpositionamplitude and phase results for multiple zones of the ROI, permittingconcurrent ablation of multiple zones of the ROI and reducing ablationprocedure duration. This step is further described with regard to FIG. 3. At 104, an ablation procedure is performed using the optimizedamplitude and phase information and the superposition results.

The method of FIG. 2 thus provides an optimization technique forselection of HIFU transducer transmitting parameters during MR-guidedtissue ablation procedures that utilize intercostal spaces, such asliver ablation. The optimization procedure yields the amplitude andphase for each element of the HIFU transducer. The optimized amplitudesand phases assure maximum heat deposition in the ROI and minimum heatdeposition on the ribs. It will be noted that, in one embodiment, thereneed be no direct element shutoff, but rather optimal amplitudes andphases may be applied to all transducer elements.

FIG. 3 illustrates a process flow for the optimization procedure basedon a spatial impulse response technique. At 120, spatial impulseresponses are calculated for the transducer probe elements on a grid ofpoints in the ROI and at the ribs locations. For clarity, FIG. 4 (below)shows the HIFU transducer, the ribs' positions and ablation ROI. At 122,a continuous wave (CW) solution is obtained by performing a Fouriertransform on the impulse responses for the given CW frequency (acousticlinear propagation is assumed for low MI's). At 124, an initial solutionfor elements' amplitudes and phases is provided to the optimizationprocedure 102. E.g., for a transducer with 128 elements, 256 values areprovided. At 126, an objective function is determined by the ratio ofacoustic pressure in the ROI and the acoustic pressure at the ribs'level. Using the input information from 122, 124, and 126, theoptimization function 102 is carried out to minimize the objectivefunction. At 128, an optimal solution for elements' amplitudes andphases is provided. It will be appreciated that similar approachesdeveloped in the frequency domain can be used, such as usingback-propagation from ROI to transducer aperture elements and accountingfor rib position.

FIG. 4 illustrates an example of a HIFU transducer array 140 positionedadjacent a patient's skin 142, with a plurality of ribs 144 impeding thetransmission of ultrasonic waves to an ROI 146 to be ablated.

FIG. 5 illustrates an example of superposition amplitude and phaseresults for ultrasonic waveforms or rays 150 so that multiple zones 152of the ROI 146 can be ablated concurrently as the HIFU array 140transmits ultrasonic waves through the patient's skin 142 and past theribs 144, minimizing the overall therapy time. FIG. 5 illustrates howthe multiple zones from the ROI can be accounted for in theoptimizations.

The following description is provided by way of example to furtherdetail the operation of the optimization module or function 102 and therelated systems and methods of FIGS. 1-5 . The HIFU array 140 hastransmitter elements (not shown) that are designated by a number n. Theyeach deliver a power I_(n), part of which is dissipated in the ROI. Thispart is proportional to the volume integral of the sonic intensity, thelocal absorption coefficient, and the attenuation of the wave beforereaching the ROI. The local intensity inside the ROI, as produced by thewave, is equal to I_(n). A number E_(ROI) is defined that isproportional to the energy delivered over a given time for a unit ofabsorption as:

$E_{ROI} = {\sum\limits_{n}{I_{n}{❘{\int_{V_{ROI}}{e^{j({\varphi_{n} - {k{❘{\overset{\rightarrow}{r} - \overset{\rightarrow}{r_{f}}}❘}}})}{dv}}}❘}^{2}}}$

-   -   E_(ROI) thus represents the total energy in the ROI, as shown in        FIG. 5 . The number n corresponds to each of the transducer        elements and I_(n) is the mean acoustic intensity in the ROI,        corresponding to each of the transducer elements. The integral        over the volume of the ROI takes into account the phase (φ_(n))        corresponding to each of the transducer elements, the wave        number k and the relative distance |{right arrow over        (r)}→{right arrow over (r_(f))}| of the point in the ROI with        respect to the natural focus of the transducer.

For the case of all the I_(n) and φ_(n) (i.e., for all transducers),respectively, being equal, a spherical wave is defined incident throughan opening cone defined by the transducer opening. If the focal point isshifted to a new position {right arrow over (r_(f1))}, and at the sametime the φ_(n) of each transducer is adjusted, the equation is of thesame form. This corresponds to the well known effect of moving the focusof an array electronically. For each position of the focus there is acorresponding set of values for the array elements defining a wavefunction for the wave delivering energy to that point of space.

When using the HIFU system for treatment, ultrasonic energy is appliedfor several minutes. During this time, heat diffuses from the point orspot being treated. Therefore, the heat need not be applied evenlyeverywhere: rather, a raster of points can be selected at some distancefrom each other that is larger than the focal spot. As a result, theinterference effects between the spots can be controlled to become smallenough to be negligible, such that:

$E_{ROI} = {\sum\limits_{mn}{❘{A_{nm}e^{j({\varphi_{nm} - {k{❘{\overset{\rightarrow}{r_{mn}} - \overset{\rightarrow}{r_{f}}}❘}}})}}❘}^{2}}$

-   -   As long as the spot being treated is reasonably close to the        natural focus of the transducer, within the beamwidths of the        individual elements, the A_(nm) amplitude is independent of m.        For the case of an unobstructed transducer, the phases are        chosen to make φ_(nm)=k|{right arrow over (r_(mn))}|+α_(m).        E_(ROI) now becomes the sum over the energy numbers of the        volume elements, as expected. The energy becomes independent of        the angle α that, as will be shown, can be used for optimizing        the more complicated case of obstructions in the near field,        e.g. by ribs.

Another parameter that can be used for optimizing the near field is thewave number k, that affects the wavelength and therefore also thediffraction pattern of the waves. k is optimized within the bandwidth ofthe HIFU transducer, which is 0.5 MHz, according to one embodiment.

For the obstructed case, the energy function is written as:

$E_{RIB} = {\sum\limits_{mn}{❘{B_{nm}e^{j({\beta_{nm} - {k{❘{\overset{\rightarrow}{r_{m}} - \overset{\rightarrow}{r_{f}}}❘}}})}}❘}^{2}}$

-   -   Next, E_(RIB) (the energy absorbed by the rib obstruction is        minimized. A suitable system of volume elements are chosen        covering the ribs for which the amplitudes B_(nm) and phases        β_(nm) are computed. Furthermore, the energy function        corresponding to the intercostal spaces can be written as:

$E_{ICST} = {\sum\limits_{mn}{❘{C_{nm}e^{j({\gamma_{nm} - {k{❘{\overset{\rightarrow}{r_{m}} - \overset{\rightarrow}{r_{f}}}❘}}})}}❘}^{2}}$

-   -   While minimizing E_(RIB), the intercostal energy E_(ICST) is        maximized. Similar to E_(RIB), a suitable system of volume        elements are chosen covering the spaces between ribs for which        the amplitudes C_(nm) and phases γ_(nm) are computed.

The objective function mentioned above becomes the maximization ofE_(ROI) and at the same time the minimization of E_(RIB) andmaximization of E_(ICST). It has been shown that suitable Eigenvectorsfor numerical optimization are represented by giving the same amplitudeto all transducer elements. The functions all correspond to focusing thefield but to different locations. The phases for the array are computedfrom said locations, but contain a common non-defined phase. TheEigen-functions are thus completely degenerate in that the energy isindependent both of the focal spot position and the common phase.

The system can now be optimized by expressing the heating in the ribregion (both in the ribs and in the intercostal spacing) using saidEigen-functions: In the ribs region their degeneracy will be lifted, andby varying the Eigenvalues so that the resulting amplitudes and phasescan concurrently ablate multiple zones of the ROI, the overall therapytime is minimized.

In this manner, in MR-guided HIFU procedures that utilize intercostalspaces, an optimal set of transmission parameters (amplitudes andphases) is generated corresponding to a given HIFU transducer position,geometry, and acoustic parameters and also accounting for the ablationROI size and position and the ribs' locations. Amplitude and phaseresults can also be super-positioned so that multiple zones of the ROIcan be ablated concurrently, minimizing the overall therapy time.

FIGS. 6-8 and the related description relate to optimization oftransducer position and element deactivation to exploit intercostalspacing during MR-guided tissue ablation. The optimization procedureaccounts for the HIFU transducer geometry and acoustic parameters,ablation ROI size and position, as well as ribs' locations in 3D. Theoptimization also takes the respiratory movement of the organ intoaccount. The ribs' locations may be provided through segmentation ofhigh resolution MR data. The output of the optimization procedure is aseries of five-degrees-of-freedom transducer locations (e.g., 3dimensions plus pitch and yaw) together with a corresponding list ofdeactivated transducer elements per position. Each transducer positionis associated with a given HIFU exposure time and energy such that aftersonication from all positions the entire ROI is ablated. In oneembodiment, the transducer positions yielded by the optimizationprocedure are sorted in the descending order of their ROI coverage sothat the treatment starts from the position with best coverage. Inanother embodiment, heating in superficial skin layers is minimized bysorting the transducer positions such that there is minimal overlapbetween consecutive active aperture footprints.

FIG. 6 illustrates a method for optimizing HIFU transducer positioningduring MR-guided tissue ablation procedures that utilize intercostalspaces, such as liver ablation. At 160, the ablation ROI is defined by auser. At 162, high resolution MR imaging is used to segment the ribs. At164, the ROI information, the ribs' positions, and the transducergeometry and acoustic parameters are used by an optimization procedureor algorithm (e.g., a set of computer-executable instructions), yieldinga series of transducer locations together with a corresponding list ofdeactivated elements at each position. The transducer locations arespecified as five-degrees-of-freedom parameters (e.g., three dimensionsplus pitch and yaw), and each transducer position is associated with agiven HIFU exposure time and energy. At 166, sonication from allpositions is carried out so that the entire ROI is ablated. At 168, MRthermal imaging is performed during sonication, to oversee the ablationprocess.

FIG. 7 illustrates a method for optimizing HIFU transducer positioning,such as occurs at step 164 of FIG. 6 . At 180, information related toHIFU transducer geometry and acoustic parameters, ablation ROI size andposition, and ribs' locations, are received as input for theoptimization algorithm. At 182, all transducer positions from whichultrasonic waves can be emitted to ablate at least a part of ROI aredetermined based on theoretical acoustic focusing and thermal depositionof the HIFU transducer. At 184, for each of the transducer positionsdetermined at 182, the elements with direct sight over the ribs aredeactivated. That is any transducer position from which an ultrasonicwave has a line of sight that passes through a rib on its way to theablation target is deactivated.

At 186, acoustic focusing and thermal deposition in the ROI arecalculated for each transducer position determined at 182, discarding ineach case the elements with a line of sight through the ribs. At 188,the transducer position with largest thermal deposition in the ROI isselected. At 190, a determination is made regarding whether the entireROI is covered (i.e., whether the entire ROI will be ablated by theactive transducer positions). If all the ROI is covered, then theoptimization is completed and, at 192, the series of active transducerlocations is output with a corresponding list of deactivated elementsper position. Each active transducer position is associated with a givenHIFU exposure time and energy (i.e., dose). If less than all of the ROIis covered, the optimization procedure returns to 186 to select thetransducer position with next largest thermal deposition in theremaining uncovered ROI.

FIG. 8 illustrates a conceptual arrangement with the HIFU array 140, theribs 144, and the ROI 146 for a given position of the array. The ribsare avoided by deactivating elements with direct sight over the ribs.The focusing and thermal deposition is calculated in the ROI consideringonly the active elements 200, and not deactivated elements 202. In thismanner, in MR-guided HIFU procedures that utilize intercostal spaces, apriori planning of the treatment is performed based on the ablation ROIsize and position, transducer geometry and acoustic parameters, and theribs' 3D position. This aspect mitigates a need for manual repositioningof the transducer array based on thermal imaging.

FIGS. 9-11 and related description further detail various embodimentsrelated to in situ sonication optimization through fast acousticsimulations. In focused ultrasound therapy, the achieved distribution ofthe thermal energy and accumulated thermal dose depend on the acousticproperties of the tissues within the sonication path. According to oneembodiment, fast acoustic in situ simulations with the acoustic pathmodeled are provided as an interactive tool for optimizing thetreatment. These simulations may be carried out immediately before thetreatment to optimize the sonication parameters. Proper,patient-specific modeling of the acoustic path improves the positioningand sharpness of the focus. Assuming the speed of sound is knownapproximately within the different tissues, these simulations can alsobe used to determine the phase for the different transducer elements inorder to achieve a tight focus. Alternatively, the element phase can bechanged interactively until the focus quality is sufficient. Workinginteractively on the tool, the user can optimize the transducerpositioning and thermal exposure, taking into account other factors suchas heat transferred into nearby organs.

Three algorithms are described in FIGS. 9-11 , which enable theabove-described functionality. The first algorithm uses stochasticsimulation to yield approximate predictions which improve withsimulation time. The second algorithm achieves fast calculation byapproximating the acoustic path as a stack of planar interfaces. Thethird algorithm estimates the acoustic field for a subset of transducerelements.

FIG. 9 illustrates a method of performing stochastic acousticsimulation, in accordance with one or more aspects described herein.According to the method, which may be stored on a computer-readablemedium as a set of computer-executable instructions, stochasticsimulation is used to quickly form a gradually improving estimate of theacoustic field. Discrete acoustic rays are randomly emitted from thetransducer elements of a HIFU array, and ray tracing techniques are usedto model their propagation towards the ROI. The obtained estimate may berough at first, and improves with time as more rays are simulated. Auser can monitor the estimate in real time and interrupt the simulationwhen ready to accept or reject the sonication setup.

Accordingly, at 220, a geometric model of a volume of interest (VOI) issegmented with acoustic parameters being specified for each subvolume.The boundary surfaces between subvolumes are extracted and discretizedinto a computationally suitable data structure, at 222. That is, eachsubvolume is specified by its respective acoustic parameters andlimiting boundary surfaces. The transducer is positioned in thesimulation domain, at 224. Phases and amplitudes of individualtransducer elements are specified, at 226. Regions of interest in theVOI are specified at 228.

A transducer element is chosen based on a predefined algorithm, at 230.In one embodiment, all elements are analyzed systematically in apredefined order, and the selection is made stochastically based on therelative amplitude of the element. At 232, a discrete computationalphonon is launched from the selected transducer element. The directionof the phonon is chosen randomly or pseudo-randomly according to apredefined directivity distribution. In one embodiment, the distributionis based on the directivity pattern of the element. Associated with thephonon are amplitude and phase, which are initialized with those of thetransducer element. At 234, the phonon is placed in a phonon buffer.

At 236, a phonon is retrieved from the phonon buffer. If the buffer isempty, the method reverts to 220. At 238, the propagation of the phononis simulated with ray tracing methods. At 240, it is determined whetherthe ray intersects with any boundary of the subvolume or a volume ofinterest.

The phase of the phonon is propagated according to the travelledacoustic distance. If hitting a boundary, the ray is split into areflected part and a transmitted part, at 242. The amplitudes, phases,and propagation directions are determined based on the angle ofincidence and the material parameters of the subvolumes on differentsides of the boundary, at 244. According to one embodiment, thepropagation directions are determined with the Snell law. In anotherembodiment, the directions are determined from a directivitydistribution. At 246, both (e.g., reflected and transmitted) phonons areplaced in the photon simulation buffer, and the method reverts to 236.

If the determination at 240 indicates that the phonon path crosses avolume of interest, the acoustic intensity map of the volume isincremented accordingly, at 248. At 250, the phonon is terminated if theamplitude of the phonon becomes lower than some criterion, or if thetrajectory of the phonon takes it outside the simulation domain, and themethod reverts to 236. In one embodiment, the phonon buffer is arrangedin such way that phonons with lowest amplitudes are chosen first.

FIG. 10 illustrates a method of performing acoustic simulation withplanar approximation, in accordance with one or more aspects describedherein. According to the method, which may be stored on acomputer-readable medium as a set of computer-executable instructions,an acoustic path is approximated as consisting of a stack of homogeneousmaterials constrained between planar interfaces. Maximal simulationspeed is achieved if the same geometry model is used for all transducerelements. For improved accuracy, the transducer elements can bepartitioned into element groups, each having their own approximation ofthe geometry.

At 270, a geometric model of a VOI is segmented with acoustic parametersbeing specified for each subvolume. The boundary surfaces betweensubvolumes are extracted and discretized into a computationally suitabledata structure, at 272. That is, each subvolume is specified by itsrespective acoustic parameters and limiting boundary surfaces. Thetransducer is positioned in the simulation domain, at 274. Phases andamplitudes of individual transducer elements are specified, at 276.Regions of interest in the VOI are specified at 278.

At 280, the transducer elements are distributed into groups. For eachgroup, an approximate geometric model is formed, where the boundarysurfaces are approximated with planes, at 282. All the transducerelements belonging to the group are traversed during this step so thatan approximate geometric model is formed for each transducer element. At284, for each element, a Fourier transformation of the sourcedistribution of the element is computed in the natural plane of thatelement. At 286, the corresponding excited field is computed. The fieldis propagated to the first boundary plane and the contributions from allelements are summed at 288. The first plane now contains the fieldexcited by the transducer element group.

At 290, the field is propagated through the tissue layer stack.According to one embodiment, if acoustic reflections are weak, as iscommon for biological tissues, multiply-reflected fields may bedisregarded. In this case, the reflected field portions are disregardedand only the transmitted portion of the field is tracked. At eachboundary, a transmission coefficient is calculated for each component ofthe Fourier-transformed field based on the wave vector of the componentand the materials parameters on different sides of the boundary, at 292.The field is multiplied with the transmission coefficient, andpropagated to the next tissue layer, at 294. The acoustic field iscomputed in the regions of interest and proceeds to the next transducerelement group, at 296.

In an alternate embodiment, if reflections need to be taken intoaccount, but are not very strong, one may resort to an iterative scheme.With each boundary, a transmitted and reflected field is associated.Initially, the first boundary contains the excited field from thetransducer element group and all other fields are empty. The fields areupdated by computing at each boundary the reflected and transmittedfield components and by propagating them to the nearby boundaries. Forweak reflections, the iteration converges rapidly.

The algorithm of FIG. 10 thus uses Fast Fourier Transformation forpropagating the acoustic field from one surface to another. Toillustrate the point, it may be assumed that there are two surfaces,each discretized into N geometric primitives. Then, propagating thefield from surface 1 to surface 2 in spatial domain involves O(N²)computations. However, if the two surfaces are planar, parallel, anduniformly discretized, a Fourier transform is applied to the field insurface 1 with O(N log₂ N) operations, the Fourier-transformed field ispropagated into surface 2 with O(N) operations, and transformed back inO(N log₂ N) operations. Moreover, if there are several layers, there isno need to carry out Fourier transformations at intermediate layers.

The requirement that all surfaces be flat and parallel is restrictivebut may be tolerable for some applications, for example for correctingfat aberration in uterine fibroid treatment. However, the requirement ofparallelism can be relaxed somewhat. For instance, theFourier-transformed field can be propagated into a slightly tilted planewith minimal computational cost. In the conversion, part of the acousticspectrum may be lost, but this part amounts to waves propagating to thesides and, in the considered application, is usually of little interest.Regarding flatness, the directivity patterns of individual transducerelements tend to be narrow. Hence, at least in the level of a singletransducer element, in many application the geometry can be approximatedas a stack of layers constrained by planar, although not necessarilyparallel, boundaries.

In one embodiment, the algorithm of FIG. 10 can be modified in such waythat either the first or the last layer can be strongly reflecting. Ifthe geometry allows for an approximation, where all planes are parallel,multiple reflections can be taken into account in a single iteration.The acoustic field is computed in the volumes of interest and proceedsto the next transducer element group.

In another embodiment, spatially depending reflectivity and transitivityare associated with any boundary. For example, a bone can be modeled byplacing a boundary at the center of the bone and specifying a zone oflow transitivity across the bone.

FIG. 11 illustrates a method for estimating the contributions of asubset of all transducer elements, in accordance with one or moreaspects described herein. At 320, the acoustic field is estimated from asmall subset of the transducer elements. At 322, estimated acousticfield contributions from additional elements are gradually added toimprove the estimate. The elements are chosen fairly evenly distributedon the transducer surface, such that the intermediate intensitydistribution is representative of the final distribution for alltransducer elements. At 324, a determination is made regarding whetherthe desired accuracy has been obtained. If so, the simulation may beinterrupted or stopped, at 326. If not, then the method reverts to 322for addition of further element acoustic field contributions. The phaseresonance of the simulated elements can be estimated using thisapproach, and thus also the focus quality.

The approach of FIG. 11 can be combined with those of either or both ofFIGS. 9 and 10 , or any other simulation procedure. Appropriate elementsare selected from which to simulate the resulting acoustic intensitydistribution. By choosing elements that are at a relatively far distancefrom each other on the transducer surface, the intensity distributionresulting from a few elements is representative of the finaldistribution of all transducer elements. The simulation may then beterminated at any point once the user is satisfied with the accuracy.

The innovation has been described with reference to several embodiments.Modifications and alterations may occur to others upon reading andunderstanding the preceding detailed description. It is intended thatthe innovation be construed as including all such modifications andalterations insofar as they come within the scope of the appended claimsor the equivalents thereof.

What is claimed is:
 1. A method of performing an in-situ sonicationsimulation for an MR-guided high intensity focused ultrasound (HIFU)ablation procedure, including: generating a patient-specific acousticpath model of a HIFU transducer that includes a plurality of HIFUtransducer elements; presenting the acoustic path model to a user via auser interface; receiving user input regarding adjustments to at leastone of (i) a position of one or more of the HIFU transducer elementsand/or (ii) a transmission phase and amplitude of the one or more of theHIFU transducer elements; and simulating a HIFU sonication of a regionof interest (ROI) using the acoustic path model and the user input,wherein simulating the HIFU sonication includes: segmenting a volume ofinterest into subvolumes; extracting and discretizing boundaries of thesubvolumes; positioning the HIFU transducer in a simulation domain;specifying phases and amplitudes for the HIFU transducer elements in theHIFU transducer; specifying one or more regions of interest (ROI) in thevolume of interest; selecting a first HIFU transducer element from theHIFU transducer elements; launching a discrete computational phonon;analyzing propagation characteristics of the launched phonon; storingthe launched phonon and path characteristic data in a phonon buffer thatstores path characteristic data for a plurality of phonons; retrieving anew phonon from the phonon buffer; simulating phonon propagation for thenew phonon; determining whether a ray defining a path of the new phononintersects one of the subvolume boundaries; dividing the ray into atransmitted portion and a reflected portion if the ray intersects theone of the subvolume boundaries; determining amplitude and phaseinformation for each of the transmitted and reflected portions of theray; and storing the amplitude and phase information for each of thetransmitted and reflected portions of the ray in the phonon buffer.
 2. Amethod of performing an in-situ sonication simulation for an MR-guidedhigh intensity focused ultrasound (HIFU) ablation procedure, including:generating a patient-specific acoustic path model of a HIFU transducerthat includes a plurality of HIFU transducer elements; presenting theacoustic path model to a user via a user interface; receiving user inputregarding adjustments to at least one of (i) a position of one or moreof the HIFU transducer elements and/or (ii) a transmission phase andamplitude of the one or more of the HIFU transducer elements; andsimulating a HIFU sonication of a region of interest (ROI) using theacoustic path model and the user input, wherein simulating the HIFUsonication includes: segmenting a volume of interest into subvolumes;extracting and discretizing boundaries of the subvolumes; positioningthe HIFU transducer in a simulation domain; specifying phases andamplitudes for the HIFU transducer elements in the HIFU transducer;specifying one or more regions of interest (ROI) in the volume ofinterest; selecting a first HIFU transducer element from the HIFUtransducer elements; launching a discrete computational phonon;analyzing propagation characteristics of the launched phonon; simulatingphonon propagation for a new phonon; determining whether a ray defininga path of the new phonon intersects one of the subvolume boundaries;dividing the ray into a transmitted portion and a reflected portion ifthe ray intersects the one of the subvolume boundaries; and determiningamplitude and phase information for each of the transmitted andreflected portions of the ray.